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Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology and robust approaches have addressed the problem of label positioning, the lengthy running time of MGFLP has not been a major focus of recent studies. Based on a hybrid of the fixed-position and sliding models, a Message Passing Interface (MPI) parallel genetic algorithm is proposed in the present study for MGFLP to label mixed types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics: label-feature conflict; label-label conflict; label association with the corresponding feature; label position priority for all three types of features. The experimental results show that the proposed algorithm outperforms the DDEGA, DDEGA-NM, and Parallel-MS in both label placement quality and computation time efficiency. Across three datasets, compared to Parallel-MS, running times decreased from 118.45 to 8.34, 45.98 to 3.51, and 20.01 to 0.43 min, with further reductions in label-label and label-feature conflicts.

Determining the design of pushbacks in an open-pit mine is a key part of optimizing the economic value of the mining project and the operational feasibility of the mine. This problem requires balancing pushbacks that have good geometric properties to ensure the smooth operation of the mining equipment and so that the scheduling of extraction maximizes the economic value by providing early access to the rich parts of the deposit. However, because of the challenging nature of the problem, practical approaches for finding the best pushbacks strongly depend on the expert criteria to ensure good operational properties. This paper introduces the Advanced Geometrically Constrained Production Scheduling Problem to account for operational space constraints, modeled as truncated cones of extraction. To find the best solution for this problem, we present a parallel genetic algorithm based on a genotype–phenotype model such that the genotype symbolizes the base block of a truncated cone, and the phenotype represents the cone itself. A central computer node evaluates these solutions, collaborating with various secondary nodes that evolve a population of feasible solutions. The PGA's efficacy was validated using comprehensive test instances from established research. The PGA solution exhibited a consistent average copper grade across periods, with its incremental phases reflecting real-world mine geometry. Moreover, the benefits of the MeanShift clustering technique were evident, suggesting effective phase-based scheduling. The PGA's approach ensures optimal resource utilization and offers insights into potential avenues for further model enhancements and fine-tuning. [ABSTRACT FROM AUTHOR]

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles. [ABSTRACT FROM AUTHOR]

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

Determining the design of pushbacks in an open-pit mine is a key part of optimizing the economic value of the mining project and the operational feasibility of the mine. This problem requires balancing pushbacks that have good geometric properties to ensure the smooth operation of the mining equipment and so that the scheduling of extraction maximizes the economic value by providing early access to the rich parts of the deposit. However, because of the challenging nature of the problem, practical approaches for finding the best pushbacks strongly depend on the expert criteria to ensure good operational properties. This paper introduces the Advanced Geometrically Constrained Production Scheduling Problem to account for operational space constraints, modeled as truncated cones of extraction. To find the best solution for this problem, we present a parallel genetic algorithm based on a genotype–phenotype model such that the genotype symbolizes the base block of a truncated cone, and the phenotype represents the cone itself. A central computer node evaluates these solutions, collaborating with various secondary nodes that evolve a population of feasible solutions. The PGA’s efficacy was validated using comprehensive test instances from established research. The PGA solution exhibited a consistent average copper grade across periods, with its incremental phases reflecting real-world mine geometry. Moreover, the benefits of the MeanShift clustering technique were evident, suggesting effective phase-based scheduling. The PGA’s approach ensures optimal resource utilization and offers insights into potential avenues for further model enhancements and fine-tuning.

Parallel versions of a genetic algorithm based on the hybrid MPI—OpenMP model are implemented to optimize circulant networks, which are of practical interest in the design of supercomputer systems and systems on a chip. An analysis of the efficiency of parallel programs with different numbers of MPI processes and OpenMP threads on a cluster of Kunpeng processors has been carried out. The speed-up of several hybrid parallel computing schemes was experimentally evaluated and analyzed. Two bottlenecks in terms of efficiency in parallel execution of the algorithm are identified and methods for their solution are proposed. By means of the parallel genetic algorithm the descriptions of circulant networks with better average distance and bisection width for the known large circulant networks were obtained. [ABSTRACT FROM AUTHOR]

This study deals with solving the Internet of Things (IoT) Service Placement Problem (SPP) in fog computing environment using metaheuristic approaches. Basically, SPP is a non-deterministic polynomial-time hard (NP-hard) with huge discrete search spaces that is often processed by heuristic and metaheuristic approaches. We proposed an Improved Parallel Genetic Algorithm (IPGA) to solve SPP and named it IPGA-SPP. Since the genetic algorithm may get stuck in local optima, we configure it in parallel with a shared memory along with several elitist operators. IPGA-SPP considers resource distribution for load balancing and prioritizes service execution to reduce latency. Also, IPGA-SPP solves the problem as a multi-objective problem by maintaining a set of Pareto solutions by making compromises between service latency, service cost, resource utilization and service time. Although many metaheuristic approaches have been developed for SPP, but satisfying the quality of service (QoS) and simultaneously guaranteeing security to overcome the constraints of fog computing has been less considered. In this regard, we equip IPGA-SPP with a two-way trust management mechanism so that clients and service providers can verify each other's trustworthiness. Therefore, the proposed scheme is a latency-aware, cost-aware and trust-aware approach to improve the deployment process in fog computing. Through simulation on a synthetic fog environment, IPGA-SPP has shown an average of 8.4% better performance compared to state-of-the-art methods such as CSA-FSPP, GA-PSO, EGA and WOA-FSP.

Introduction. The paper considers some features of the parallel implementation of a multipopulation genetic algorithm, as well as approaches to its optimization. The results of experiments with the use of a different number of processors and different methods of generating initial populations are presented in order to optimize the algorithm according to several criteria (assessment of the use of computational and time resources). On the example of a specific test problem, estimates are given for choosing the optimal number of processors to obtain the desired result. The purpose of this work is to conduct experiments with a given test problem with a different number of processors and alternative methods for generating the initial population to evaluate the effectiveness of the algorithm. Results. For the test problem, to obtain a result of 90–94 % of the optimum, the most efficient in terms of computing resources is the use of 4 processors with an algorithm for uniform scanning of the space of factor values. To achieve a result exceeding 94 % and optimize by K1 (computational resources), 8 processors and an algorithm for uniform scanning of the space of factor values showed the best result. If we also take into account the criterion of time resources K2, then to achieve 90–98 % of the optimum, it is necessary to use 8 processors, for 99–100 % 12 or 16 processes, depending on C1 and C2 (cost of computational and time resources respectively). Conclusions. Performed experiments show that the algorithm of uniform scanning of the space of factor values is more efficient than the random method of generating the initial population. Experiments also showed that in order to achieve the maximum efficiency of PMGA, the number of processors must be chosen depending on the desired result precision.

Airline crew scheduling is a very important part of the operational planning of commercial airlines, but it is a linear integer programming problem with multi-constraints. Traditionally, the airline crew scheduling problem is determined by solving the crew pairing problem (CPP) and the crew rostering problem (CRP), sequentially. In this paper, we propose a new heuristic solver based on the parallel genetic algorithm and an innovative crew scheduling algorithm, which improves traditional crew scheduling by integrating CPP and CRP into a single problem. The innovative scheduling method includes a global heuristic search and an adjustment for flights and crew so as to realize crew scheduling. The parallel genetic algorithm is used to divide the population into multiple threads for parallel calculation and to optimize the randomly generated flight sequence to maximize the number of flights that meet the crew configuration. Compared with the genetic algorithm, CPLEX and Gurobi, it shows high optimization efficiency, with a time reduction of 16.57–85.82%. The experiment shows that our crew utilization ratio is higher than that for traditional solvers, achieving almost 44 flights per month, with good scalability and stability in both 206 and 13,954 flight datasets, and can better manage airline crew scheduling in times of crew scarcity. [ABSTRACT FROM AUTHOR]

Summary: Network virtualization (NV) has ubiquitously emerged as an indispensable attribute to enable the success of the forthcoming virtualized networks (eg, 5G network and smart Internet of Things [IoT]). Virtual network embedding (VNE) is the major challenge in NV that allows multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. A great number of VNE algorithms have been proposed, but over the past decades, most of them are only targeting for VNE node mapping. In this paper, we propose two distributed parallel genetic algorithms, which are based on two versions of crossover and mutation schemes, for online VN link embedding problems with low latency and high efficiency. Furthermore, we conduct a time analysis on the executing time of independently distributed parallel computing machines in details. This comprehensive analysis validates the parallel computing scalability on an identical number of predefined parallel machines. Extensive simulations have shown that our proposed algorithms can achieve better performance than integer linear programming (ILP)–based solutions while meeting the stringent time requirements for online VN embedding applications. Our proposed algorithms yield superior performance in running time with 32.78% up to 1727.8% faster than existing popular VNE algorithms. Additionally, the theoretical analysis indicates that the execution time can be reduced to logarithmic times by applying proposed distributed parallel algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose three reactive distillation-based processes to separate Serafimov's class 2.0–2b mixtures containing methanol/methyl acetate. • Methyl acetate transesterification eliminates methyl acetate and coproduces high-value products. • Integrate extractive/pre-separation distillation and reactive distillation. • Two-column pre-separation-reactive distillation is preferred. • Use parallel genetic algorithms to optimize processes. The treatment of waste streams is an important research topic for the sustainable development of chemical process. Recently, reactive distillation-based (RD) processes with ethylene oxide hydrolysis have been developed to separate water-containing azeotropic mixtures. To separate Serafimov's class 2.0–2b mixtures containing methanol/methyl acetate, a methyl acetate transesterification reaction between propylene glycol monomethyl ether is introduced to convert methyl acetate to methanol and coproduce produce propylene glycol monomethyl ether acetate as advanced solvents. Three-column reactive-extractive distillation (TCRED) and extractive-reactive distillation (TCERD), and double-column pre-separation-reactive distillation (DCPSRD) processes, are proposed. Then, we use a parallel genetic algorithm to optimize processes to maximize total net revenue. The case study is methanol/methyl acetate/ethyl acetate. Though TCRED is not suitable due to the occurrence of ethyl acetate transesterification reactions, to compare economic performances of three RD-based processes, TCRED is regarded as a pseudo process. The total net revenue of three RD-based processes is relatively larger than (∼20 % increase) that of the conventional extractive distillation process. Compared with TCRED process, TCERD and DCPSRD can achieve $582336 and $1045995 increase in TNR, 27.43 % and 49.99 % TAC reduction, respectively. In summary, proposed RD-based processes are promising to separate Serafimov's class 2.0–2b mixtures containing methanol/methyl acetate, especially TCRED and DCPSRD. [ABSTRACT FROM AUTHOR]

This research introduces an innovative approach to the design and simulation of bio-based laminated structures, specifically focusing on glue-laminated bamboo (glubam) used in roof trusses. Our study fills a critical gap by investigating the mechanical behaviors of bolted connections in bamboo-based structures, which have not been comprehensively studied before. We employ a dual-phase methodology: initially, cyclic tests on bolted steel to glubam joints assess their hysteretic behavior, followed by tests on glubam planar roof trusses to evaluate structural responses under practical conditions. Our novel contribution is the development of a simplified mechanical-based hysteretic model, incorporating connector and spring elements in series or parallel within the ABAQUS software. This model significantly improves on existing models by allowing for initial calibration through a parallel genetic algorithm (PGA), enhancing both accuracy and efficiency. Subsequent incorporation of this model into the simulation of truss joints enabled the creation of an advanced hybrid roof truss model within ABAQUS. The final stage of our research demonstrates the application of a PGA-based model-updating framework, which substantially increases the model's predictive accuracy. This work not only advances the understanding of structural behavior in bio-based construction materials but also introduces a robust framework for model updating that can be applied to other engineering simulations, contributing to more sustainable and resource-efficient construction practices. • Axial behavior of bolted steel to Glued Laminated Bamboo (glubam) joints. • Simplified mechanical-based hysteretic models of truss joints developed in ABAQUS. • Simplified structural FE model of roof truss developed in ABAQUS. • Advanced hybrid roof truss model developed within ABAQUS. • Advanced sustainable Civil Engineering practices. [ABSTRACT FROM AUTHOR]

Bolted steel to laminated bio-based material connections experience significant performance challenges due to the nonlinear response and high stress concentrations at their joints. This paper introduces an innovative 3D plasticity-fracture continuum Finite Element (FE) model that significantly advances the simulation of such truss joints by integrating Hill's yielding criteria with an element removal methodology for fracture simulation. This novel approach captures both plastic and fracture behaviors simultaneously, a capability not sufficiently addressed in existing models. We detail the theoretical framework for these models, including the derivation of constitutive equations and the algorithms necessary for their implementation in ABAQUS. Additionally, it is provided a low-fidelity modeling of truss joints, offering a comprehensive analysis of connector elements, joint models, and parametric modeling via Python scripting. The model's efficacy is demonstrated through identification of connection and of hybrid planar trusses under cyclic loading, which validates the practical applicability of the method. To optimize computational efficiency, we developed a Parallel Genetic Algorithm (PGA) that integrates seamlessly with ABAQUS and Python to facilitate parameter calibration. This integration not only enhances the model's accuracy but also reduces computational load, making it feasible for complex engineering applications. Our findings illustrate a significant improvement in modeling accuracy and efficiency, establishing a robust methodology for analyzing truss joints in bio-based construction materials. • 3D FE model for bolted steel to timber/glubam connections. • Merged Hill's criteria with element removal for fractures. • Integrated ABAQUS and Python for multi-threaded analysis. • Utilized parallel genetic algorithm (PGA) for efficient calibration of connection models. • Assessed model on planar trusses under cyclic loading. [ABSTRACT FROM AUTHOR]

This study deals with solving the Internet of Things (IoT) Service Placement Problem (SPP) in fog computing environment using metaheuristic approaches. Basically, SPP is a non-deterministic polynomial-time hard (NP-hard) with huge discrete search spaces that is often processed by heuristic and metaheuristic approaches. We proposed an Improved Parallel Genetic Algorithm (IPGA) to solve SPP and named it IPGA-SPP. Since the genetic algorithm may get stuck in local optima, we configure it in parallel with a shared memory along with several elitist operators. IPGA-SPP considers resource distribution for load balancing and prioritizes service execution to reduce latency. Also, IPGA-SPP solves the problem as a multi-objective problem by maintaining a set of Pareto solutions by making compromises between service latency, service cost, resource utilization and service time. Although many metaheuristic approaches have been developed for SPP, but satisfying the quality of service (QoS) and simultaneously guaranteeing security to overcome the constraints of fog computing has been less considered. In this regard, we equip IPGA-SPP with a two-way trust management mechanism so that clients and service providers can verify each other's trustworthiness. Therefore, the proposed scheme is a latency-aware, cost-aware and trust-aware approach to improve the deployment process in fog computing. Through simulation on a synthetic fog environment, IPGA-SPP has shown an average of 8.4% better performance compared to state-of-the-art methods such as CSA-FSPP, GA-PSO, EGA and WOA-FSP. [ABSTRACT FROM AUTHOR]

The optimization process of hydrogen liquefaction process is complex and time-consuming. In order to solve the above problems and improve the solving efficiency of the optimization process, this paper proposed the method of using parallel genetic algorithm combined with simulation software for optimization. Parallel genetic algorithm effectively overcomes the premature convergence of standard genetic algorithm and has strong global search ability. The parallel processing accelerates the optimization process by 2.01 times, and saves 50.22% of the time compared with the serial calculation. It not only improves the solving speed, but also improves the solving quality and the calculation performance. After optimization, the specific energy consumption of the system is reduced by 52.26%, the exergy loss is reduced by 49.81%, the heat exchange efficiency is improved, and the process performance of the system is improved. This work has reference significance for hydrogen liquefaction process optimization using parallel genetic algorithm. • Optimization of hydrogen liquefaction process using parallel genetic algorithm and simulation software. • Parallel genetic algorithm overcomes premature convergence and improves solution quality. • Parallel genetic algorithms can improve the solving speed and have good computing performance. • Optimization can improve process performance of hydrogen liquefaction system. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set of sensors to either process the collected data locally or send them to the cloud for remote processing. Unfortunately, cloud datacenters are located far away from IoT devices, and consequently, the transmission of IoT data may be delayed. In this paper, we investigate fog computing, which is a new paradigm that overcomes many issues of cloud computing. More importantly, dynamic task offloading in fog computing is a challenging problem that requires an optimal decision for processing the tasks that are generated in each time slot. Thus, exact optimization methods based on Lyapunov function have been widely used for solving dynamic offloading which represents an NP hard problem. To overcome the scalability issue of exact optimization techniques, we have explored famous population based meta-heuristics for optimizing the offloading process of a set of dynamic tasks using Orthogonal Frequency Division Multiplexing (OFDM) communication. Hence, a parallel multi-threading framework is proposed for generating the optimal offloading solution while selecting the best sub-carrier for each offloaded task. More importantly, our contribution associates a thread for each IoT device and generates a population of random solutions. Next, each population is updated and evaluated according to the proposed fitness function that considers a tradeoff between the delay and energy consumption. Upon the arrival of new tasks at each time slot, an evaluation is performed for maintaining some individuals of the previous population while generating new individuals based on some criteria. Our results have been compared to the results achieved using Lyapunov optimization. They demonstrate the convergence of the fitness function, the scalability of the parallel Particle Swarm Optimization (PSO) approach, and the performance in terms of the offline error and the execution cost. [ABSTRACT FROM AUTHOR]

Uncertainties from neighboring rival’s reservoirs challenge hydropower companies in participating in competitive markets. Cooperative behaviors are generally impractical due to stakeholders’ self-interest and regulatory requirements. Considering this obstacle, this paper proposes a data-driven bilevel model, in a competitive context, to estimate the operational information of the neighboring rival’s reservoir, including its historical operating states and operational functions. The proposed bilevel model is an inverse problem of the conventional hydropower scheduling model. The upper-level model is designed to find the most appropriate operational parameters of the estimated reservoir that fit its historical generation volumes. The lower-level model simulates the profit-maxing operation of the estimated reservoir. Since the lower simulating model is nonconvex, an Enhanced Parallel Genetic Algorithm (EPGA) is proposed. It avoids infeasible situations through several strategies and uses multiple CPU threads simultaneously in solving. A case study in China’s market demonstrates that the proposed model and solving method can efficiently obtain accurate state series and (near-)optimal operational parameters. More experiments are also taken to validate the parallel design.

With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods; however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods. [ABSTRACT FROM AUTHOR]

Summary: In this article, we present a parallel graphical processing unit (GPU)‐based genetic algorithm (GA) for solving the resource‐constrained multi‐project scheduling problem (RCMPSP). We assumed that activity pre‐emption is not allowed. Problem is modeled in a portfolio of projects where precedence and resource constraints affect the portfolio duration. We also assume that the durations, availability of resources are deterministic and portfolio has a static nature. The objective in this article is to find a start time for each activity of the project so that the portfolio duration is minimized, while satisfying precedence relations and resource availabilities within a reasonable amount of time for small and large problem instances. In order to compare the efficiency of the proposed parallel GPU‐based GA, problem is solved together with a CPU and a GPU. The results showed that GPU‐based parallel GA has high potential for improving the performance of GAs for the RCMPSP particularly, for large‐scale problems. [ABSTRACT FROM AUTHOR]

Today, the use of intelligent systems in medical diagnosis is gradually increasing. These systems lead to a reduction in error, which may be experienced by inexperienced experts. In this study, the use of artificial intelligent systems in predicting and diagnosing breast cancer, which is one of the most common cancers among women, is being considered. In this research, the diagnosis of breast cancer is performed with a two-stage approach. In the first step, the two parameters of the effective properties and the number of secret layer nodes for optimizing the MLP neural network are simultaneously optimized by a genetic algorithm. Then, using selected features and number of hidden layer nodes, a MLP neural network modeling model is developed for diagnosis of breast cancer in the second step. Here, a FinGrain parallels genetic algorithm based on optimized parameters is used to adjust the weight of the MLP neural network. The evaluation of the experiments shows that the proposed method compared to the two GAANN and CAFS methods on the WBCD dataset yielded better results and reported an accuracy of 98.72% in the average time.

The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set of sensors to either process the collected data locally or send them to the cloud for remote processing. Unfortunately, cloud datacenters are located far away from IoT devices, and consequently, the transmission of IoT data may be delayed. In this paper, we investigate fog computing, which is a new paradigm that overcomes many issues of cloud computing. More importantly, dynamic task offloading in fog computing is a challenging problem that requires an optimal decision for processing the tasks that are generated in each time slot. Thus, exact optimization methods based on Lyapunov function have been widely used for solving dynamic offloading which represents an NP hard problem. To overcome the scalability issue of exact optimization techniques, we have explored famous population based meta-heuristics for optimizing the offloading process of a set of dynamic tasks using Orthogonal Frequency Division Multiplexing (OFDM) communication. Hence, a parallel multi-threading framework is proposed for generating the optimal offloading solution while selecting the best sub-carrier for each offloaded task. More importantly, our contribution associates a thread for each IoT device and generates a population of random solutions. Next, each population is updated and evaluated according to the proposed fitness function that considers a tradeoff between the delay and energy consumption. Upon the arrival of new tasks at each time slot, an evaluation is performed for maintaining some individuals of the previous population while generating new individuals based on some criteria. Our results have been compared to the results achieved using Lyapunov optimization. They demonstrate the convergence of the fitness function, the scalability of the parallel Particle Swarm Optimization (PSO) approach, and the performance in terms of the offline error and the execution cost.

HR managers require efficient and effective ways to move forward from traditional recruiting processes and select the right candidates for the right jobs. The kind of staff recruitment that we deal with in this paper is the massive recruitment under several constraints modeled by with the objective of improving the company's performance. It is modeled as a multiple knapsack problem known as an NP-hard problem. Henceforth, solving this problem by a basic GA leads to an approximate solution with large CPU time consumption. For this purpose, we propose a parallel genetic approach to recruitment in order to generate the best quality solution in a reduced CPU time that ensures a better compatibility with what the company is looking for. Operationally, the results obtained in different tests validate the performance of our parallel genetic algorithm for the best optimization of human resources recruitment. [ABSTRACT FROM AUTHOR]

Industrial optimization problems are usually difficult to solve due to complexity and high number of constraints. Evolutionary algorithms are a conventional method to solve these problems. However, many industrial applications are real-time or we need to find a feasible optima solution in a limited time. Parallel genetic algorithm is a method to utilize properties of the genetic algorithm and parallel processing and implementation of a fast evolutionary algorithm. Controller Area Network (CAN) protocol is widely used in various industries such as automotive, medical, aerospace. In this paper, we implement a multiple-population coarse-grained parallel genetic algorithm on CAN bus to improve speed and performance of the conventional genetic algorithm which is asynchronous distributed multi-master. Evaluation criteria such as speed up, efficiency, serial fraction and reliability are calculated for the proposed parallel processing which is used for optimization problem of five benchmark functions. And finally, this structure is compared with the master–slave model. The proposed structure is created conditions for improving network reliability with very low cost of communication. [ABSTRACT FROM AUTHOR]

In view of the fact that the scheduling parallel genetic algorithm is affected by the parameters and the performance of the same scale scheduling problem is very different, the number of subgroups, the number of subgroup individuals, the generation gap, 8 factors such as cross rate affect the running time. Using analysis of variance and range analysis methods, the primary and secondary order of factors are studied, the interaction between generation gap and mutation rate, crossover rate and mutation rate is analyzed, the optimal level combination of parameters is determined, and the FT under the selected parameters is tested A typical scheduling problem. The research results show that the use of orthogonal test instead of full factor test to optimize the scheduling of parallel genetic algorithm parameters can effectively improve the evolution process and shorten the calculation time on the basis of ensuring the quality of the algorithm. [ABSTRACT FROM AUTHOR]

Scheduling is a process of mapping resources to tasks and it's objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are the jobs submitted to the processor. The main objectives of multiprocessor scheduling are reducing schedule length, reducing the overall energy consumption, reducing the temperature, reducing failure rates and so on. A Hybrid dual-objective parallel genetic algorithm is applied in the proposed work. Makespan and energy consumption are the two objectives considered. The proposed algorithm determines the global optimal solutions by generating the initial population using some heuristics and then performing parallel genetic operations on it. The main aim of employing parallelism is to find a global optimum solution by avoiding premature convergence in a local optimum and to reduce the running time of the algorithm. Hill climbing is also used in addition, to avoid local optimum solutions. The proposed algorithm balances the tradeoff between energy consumption and makespan according to the inclinations of the users by following weighted sum methodology. Our experimental results demonstrate that the proposed algorithm outperforms the other existing algorithms in terms of both makespan and energy consumption by incurring less running time. [ABSTRACT FROM AUTHOR]

The Sunway TaihuLight, which is composed of Sunway many-core processors, is currently the highest performance supercomputer in China. It can provide a hardware platform for NSGA-Ⅱ to solve the large-scale problems. Considering the architecture of Sunway many-core processor, this paper designed an island combined with enhanced master-slave hybrid parallel NSGA-Ⅱ algorithm. Based on the master-slave mode, it used register communication to realize the sharing of local data memory of CPE in a core group. It optimized the algorithm process and parallelized more algorithm modules on CPE. By means of DMA transmission, vectorization, double buffering and storage optimization, it significantly increased the speedup. The experiments show that the optimized parallel NSGA-Ⅱ has good speedup and scalability on the Sunway many-core processors. [ABSTRACT FROM AUTHOR]

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage manufacturing system so that operations of a given lot can overlap. This technique can reduce the manufacturing makespan and is an effective tool in time-based manufacturing. Research on lot streaming models and solution procedures for flexible jobshops has been limited. The flexible jobshop scheduling problem is an extension of the classical jobshop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. In this paper we develop a lot streaming model for a flexible jobshop environment. The model considers several pragmatic issues such as sequence-dependent setup times, the attached or detached nature of the setups, the machine release date and the lag time. In order to solve the developed model efficiently, an island-model parallel genetic algorithm is proposed. Numerical examples are presented to demonstrate the features of the proposed model and compare the computational performance of the parallel genetic algorithm with the sequential algorithm. The results are very encouraging. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GA) is combined with finite-element method for the first time as an alternative method to invert gravity anomaly data for reconstructing the 3D density structure in the subsurface. The computational efficiency is significantly improved by storing the coefficient matrix and using it in all the forward calculations, and by dividing the region of interest into many sub-regions and applying the GA to the sub-regions in parallel. Central Taiwan - a geologically complex region - is used as an example to demonstrate the utility of the method. A crustal block, 120 x 150 km2 in area and 34 km in thickness, is represented by a finite-element model of 76,500 cubic elements, each 2x2x2 km3 in size. An initial density model is reconstructed from the regional 3D tomographic seismic velocity, using an empirical relation between velocity and density. The difference between the calculated and the observed gravity anomaly (i.e., the residual anomaly) shows an elongated minimum of large magnitude that extends along the axis of the Taiwan mountain belt. Among the interpretative models tested, the best model shows a crustal root beneath the axis of the Central Ranges and a density contrast of 400 or 500 kg/m3 across the Moho. Both predictions appear to be supported by independent seismological and laboratory evidences.

A method to estimate frequency domain soil parameters of horizontally multilayered earth is developed. Cole–Cole model of complex conductivity form is adopted to describe the frequency dependence of soil parameters. The estimation of frequency domain soil parameters includes two stages. In the first stage, soil‐layered structure is determined under the DC field by the interpretation of resistivity sounding data. The obtained model parameters in this stage include the DC soil conductivity and thickness of soil layers. In the second stage, Cole–Cole model parameters are estimated based on the soil‐layered model obtained in the first stage. The theoretical formula of complex apparent resistivity is derived from Green's function with considering dynamic‐state field theory. Since dynamic‐state field is considered in the inversion algorithm, this method can be extended into high‐frequency domain. Discrete complex image method is used to calculate Sommerfeld integral quickly. In order to remove electromagnetic (EM) coupling between cables at high frequencies, a new electrode configuration method is proposed. Genetic algorithm is improved by combining with parallel computing. The parallel genetic algorithm is applied to the optimisation of model parameters. [ABSTRACT FROM AUTHOR]

Gnomic information continues to flood, and this trend comes in the wake of the life sciences' rapid development. The eventuality has been an increase in the demand for more scalable and faster searching techniques, with the demand also proving urgent. Whereas a faster algorithm could be used to search biomedical data, the process of making gene prediction remains challenging. Particularly, the searching of biomedical data has been affirmed to be a simple gradient base approach. Therefore, indexing has been investigated with the aim of achieving a fast finite conventional rate. With biomedical expressed datasheet at hand, data-based large sequence identification has been achieved via the prefix pattern gene search algorithm. Imperative to note is that real-value expression matrices can replace microarray experimental gene expression data. To ensure that the genomic dataset's querying exhibits reductions in the overall retrieval time and that the time used for pattern array building is sped up, parallel partitioned methods have gained application. Notably, the central merit accruing from the latter method is that the majority of unrelated sequences are skipped. Also, these methods ensure that the real search problems are only decomposed to establish original database fractions. To ensure that the establishment of the gene's hidden information and similar characteristics is enhanced, large genetic data patterns are required. [ABSTRACT FROM AUTHOR]

Highlights • Reconfiguration cost is firstly developed during the components rearrangement. • An integrated method is proposed through rearrangement and replacement. • A novel fitness function is presented considering the distance from ideal point. • Parallel genetic algorithm is introduced to solve the multi-objective model. Abstract Reconfigurable systems have been widely used in practical engineering, especially for the reconfigurable computing systems and reconfigurable manufacturing systems. The reliability of reconfigurable systems can be improved by components replacement or components rearrangement without changing their reliability. Combining the advantages of the rearrangement method and replacement method, an integrated method is proposed to improve the reconfigurable system reliability cost-effectively in this paper. Then, a 0–1 integer programming model of multi-objective optimization is established to obtain the reconfiguration with maximum system reliability and minimum reconfiguration cost based on the integrated method. The coarse-grained parallel genetic algorithm (CPGA) is introduced to solve the multi-objective model, while the multiple objectives problem can be converted into a single objective problem through the novel fitness function. Finally, three examples based on the production monitoring system are implemented to illustrate the effectiveness of the CPGA comparing with the replacement based genetic algorithm. The changes of optimal reconfigurations with different parameters of the fitness function and different pre-determined system reliability are also discussed based on the examples. [ABSTRACT FROM AUTHOR]